December 2020 arXiv papers — page 137

Hamidreza Hashempour, Kiyanoush Nazari, Fangxun Zhong, Amir Ghalamzan E.

Many robotic tasks are still teleoperated since automating them is very time consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce programming time and cost. However, conventional RLfD approaches are not directly applicable to many robotic tasks, e.g. robotic suturing with minimally invasive robots, as they require a time-consuming pr

Stéphane André, Julien Boisse, Camille Noûs

FFT-based solvers are increasingly used by many researcher groups interested in modelling the mechanical behavior associated to a heterogeneous microstructure. A development is reported here that concerns the viscoelastic behavior of composite structures generally studied experimentally through Dynamic Mechanical Analysis (DMA). A parallelized computation co

Jérôme Darmont, Cécile Favre, Sabine Loudcher, Camille Noûs

Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully master this process, we propose the use of data lakes as a solution to data siloing and big data variety problems. We descri

B. Samhita Varambally, Naman Sehgal

With the complexity of Integrated Circuits increasing, design verification has become the most time consuming part of the ASIC design flow. Nearly 70% of the SoC design cycle is consumed by verification. The most commonly used approach to test all corner cases is through the use of Constrained Random Verification. Random stimulus is given in order to hit all

Haoyu Chu, Shikui Wei, Yao Zhao

Recent studies have shown that deep neural networks are vulnerable to adversarial examples, but most of the methods proposed to defense adversarial examples cannot solve this problem fundamentally. In this paper, we theoretically prove that there is an upper bound for neural networks with identity mappings to constrain the error caused by adversarial noises.

Zhyrair Gevorkian, Mher Davtyan

Radiation from a charged particle moving in a medium with Maxwell fish eye refraction index profile is considered. It is shown that the radiation spectrum has a discrete character. The main emitted wavelength is proportional to the refractive profiles radius and has a dipole character in a regular medium. Cherenkov like threshold velocity is established. A c

Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni

As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in which an aggregator orchestrates multiple parties to train a global model without sharing their training data. In this paper, we discuss causes of bias in FL and propose three pre

Cheng-Han Hsieh, Héctor G. Arce, Diego Mardones, Shuo Kong

Angular momentum is one of the most important physical quantities that govern star formation. The initial angular momentum of a core may be responsible for its fragmentation and can have an influence on the size of the protoplanetary disk. To understand how cores obtain their initial angular momentum, it is important to study the angular momentum of filament

Huan Zhang, Wei Ye, Chaoping Wei, Cunjin Liu

We propose a theoretical scheme to improve the resolution and precision of phase measurement with parity detection in the Mach-Zehnder interferometer by using a nonclassical input state which is generated by applying a number-conserving generalized superposition of products (GSP) operation, (saa^{†}+ta^{†}a)^{m} with s^2+t^2=1, on two-mode squeezed vacuum (T

Nikolaos G. Fytas

We study the transport properties of low-energy (quasi)particles ballistically traversing normal and Andreev two-dimensional open cavities with a Sinai-billiard shape. We consider four different geometrical setups and focus on the dependence of transport on the strength of an applied magnetic field. By solving the classical equations of motion for each setup

Wangshu Zhu, Andre Rosendo

Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers from performance instability and optimizat

Vladimir Shikhman

We study mathematical programs with switching constraints (MPSC)from the topological perspective. Two basic theorems from Morse theory are proved. Outside the W-stationary point set, continuous defor-mation of lower level sets can be performed. However, when passing a W-stationary level, the topology of the lower level set changes via the attachment of a w-d

Junshan Wang, Ziyao Li, Qingqing Long, Weiyu Zhang

Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. While existing methods tend to focus on preserving structur

Hiroshi Takatsu, Masayuki Ochi, Naoya Yamashina, Morito Namba

We report the epitaxial thin film synthesis of SrCu$_3$O$_4$ with infinitely stacked Cu$_3$O$_4$ layers composed of edge-sharing CuO$_4$ square-planes, using molecular beam epitaxy. Experimental and theoretical characterizations showed that this material is a metastable phase that can exist by applying tensile biaxial strain from the (001)-SrTiO$_3$ substrat

Hideki Ishihara, Satsuki Matsuno

We construct stationary solutions to the Einstein-Maxwell-current system by using the Sasakian manifold for the three-dimensional space. Both the magnetic field and the electric current in the solution are specified by the contact form of the Sasakian manifold. The solutions contain an arbitrary function that describes inhomogeneity of the number density of

Tatsuki Fushimi, Kenta Yamamoto, Yoichi Ochiai

Acoustic holograms are the keystone of modern acoustics. It encodes three-dimensional acoustic fields in two dimensions, and its quality determine the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we

Junwei Liang, Liangliang Cao, Xuehan Xiong, Ting Yu

This paper studies how to introduce viewpoint-invariant feature representations that can help action recognition and detection. Although we have witnessed great progress of action recognition in the past decade, it remains challenging yet interesting how to efficiently model the geometric variations in large scale datasets. This paper proposes a novel Spatia

Hiroshi Takatsu, Naoya Yamashina, Daisuke Shiga, Ryu Yukawa

SrMoO$_3$ is a promising material for its excellent electrical conductivity, but growing high-quality thin films remains a challenge. Here we synthesized epitaxial films of SrMoO$_3$ using the molecular beam epitaxy (MBE) technique under a low oxygen-flow rate. Introduction of SrTiO$_3$ buffer layers of 4--8 unit cells between the film and the (001)-oriented

Keiran Paster, Sheila A. McIlraith, Jimmy Ba

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capacity on task-relevant dynamics, while sim

Yuta Seo, Satoru Masubuchi, Eisuke Watanabe, Momoko Onodera

We describe a technique for making one-dimensional ohmic contacts to individual graphene layers encapsulated in hexagonal boron nitride (h-BN) using CF4 and O2 plasmas. The high etch selectivity of h-BN against graphene (>1000) is achieved by increasing the plasma pressure, which enables etching of h-BN, while graphene acts as an etch stop to protect underly

K. R. Covey, K. A. Larson, G. J. Herczeg, C. F. Manara

AA Tau is a classical T Tauri star with a highly inclined, warped circumstellar disk. For decades, AA Tau exhibited photometric and spectroscopic variability that were successfully modelled as occultations of the primary star by circumstellar material. In 2011, AA Tau entered an extended faint state, presumably due to enhanced levels of circumstellar dust. W

Takeshi Teshima, Koichi Tojo, Masahiro Ikeda, Isao Ishikawa

Neural ordinary differential equations (NODEs) is an invertible neural network architecture promising for its free-form Jacobian and the availability of a tractable Jacobian determinant estimator. Recently, the representation power of NODEs has been partly uncovered: they form an $L^p$-universal approximator for continuous maps under certain conditions. Howe

Xiayimei Han

In this thesis, I apply the Green-Griffiths-Kerr classification of Hodge representations to enumerate the Lie algebra Hodge representations of CY 3-fold type

Shusaku Imajo, Chao Dong, Akira Matsuo, Koichi Kindo

We have developed a new calorimeter for measuring thermodynamic properties in pulsed magnetic fields. An instrumental design is described along with the construction details including the sensitivity of a RuO2 thermometer. The operation of the calorimeter is demonstrated by measuring heat capacity of three samples, pure Germanium, CeCu2Ge2, and $κ$-(BEDT-TTF

Yusuke Hama

Quantum computers have now appeared in our society and are utilized for the investigation of science and engineering. At present, they have been built as intermediate-size computers containing about fifty qubits and are weak against noise effects. Hence, they are called noisy-intermediate scale quantum devices. In order to accomplish efficient quantum comput

Parshwa Shah, Arpit Garg, Vandit Gajjar

A person is usually characterized by descriptors like age, gender, height, cloth type, pattern, color, etc. Such descriptors are known as attributes and/or soft-biometrics. They link the semantic gap between a person's description and retrieval in video surveillance. Retrieving a specific person with the query of semantic description has an important app

Hiroshi Takatsu, Naoya Yamashina, Masayuki Ochi, Hsin-Hui Huang

A double-layered perovskite oxide Sr$_3$Mo$_2$O$_7$ is considered a "hidden ladder" system with wide and narrow bands near the Fermi level, for which high-$T_{\rm c}$ superconductivity is expected. However, the difficulty in synthesis, especially in the preparation of samples without oxygen deficiency, can hinder the observation of superconductivity.

Charles Patrick Martin, Alexander Refsum Jensenius, Jim Torresen

This paper describes the process of developing a standstill performance work using the Myo gesture control armband and the Bela embedded computing platform. The combination of Myo and Bela allows a portable and extensible version of the standstill performance concept while introducing muscle tension as an additional control parameter. We describe the technic

Jalal Arabneydi, Aditya Mahajan

In this paper, we investigate team optimal control of coupled major-minor subsystems with mean-field sharing. In such a model, there is one major subsystem that directly influences the dynamics of $n$ homogeneous minor subsystems; however, the minor subsystems influence the dynamics of the major subsystem and each other only through their mean behaviour (ind

Tao Li, Ling Pei, Yan Xiang, Qi Wu

Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurate GNSS operation mode independent of bas

Kexin Chen, Chi Seng Pun, Hoi Ying Wong

Social distancing has been the only effective way to contain the spread of an infectious disease prior to the availability of the pharmaceutical treatment. It can lower the infection rate of the disease at the economic cost. A pandemic crisis like COVID-19, however, has posed a dilemma to the policymakers since a long-term restrictive social distancing or ev

Ilya Archakov, Peter Reinhard Hansen

We introduce a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive definiteness is an innate property. This parametrization can be viewed as a generalization of Fisther's Z-transformation to higher dimensions and has a wide range of po

Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu

Why do biased predictions arise? What interventions can prevent them? We evaluate 8.2 million algorithmic predictions of math performance from $\approx$400 AI engineers, each of whom developed an algorithm under a randomly assigned experimental condition. Our treatment arms modified programmers' incentives, training data, awareness, and/or technical know

Bo Cowgill, Fabrizio Dell'Acqua, Sandra Matz

How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI

Bo Wei, Sila Cetinkaya, Daren B. H. Cline

We consider a distribution warehouse where both the inbound inventory replenishment and outbound dispatch decisions are subject to fixed (as well as per-unit) transportation charges and demand is stochastic. In order to realize scale economies associated with transportation operations both on the outbound and inbound sides, dispatch schedules must be synchro

Pietro Dall'Olio, Axel Weber

Within the refined Gribov-Zwanziger scenario for four-dimensional Yang-Mills theory in the Landau gauge, a gluon mass term is generated from the restriction of the gauge field configurations to the first Gribov region. Tissier and Wschebor have pointed out that simply adding a gluon mass term to the usual Faddeev-Popov action yields one-loop renormalization

Niall Bootland, Victorita Dolean, Frédéric Nataf, Pierre-Henri Tournier

In this paper we develop and analyse domain decomposition methods for linear systems of equations arising from conforming finite element discretisations of positive Maxwell-type equations. Convergence of domain decomposition methods rely heavily on the efficiency of the coarse space used in the second level. We design adaptive coarse spaces that complement t

Leilei Cao, Tong Yang, Yixu Wang, Bo Yan

Inpainting high-resolution images with large holes challenges existing deep learning based image inpainting methods. We present a novel framework -- PyramidFill for high-resolution image inpainting task, which explicitly disentangles content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolutio

Smitha Vishveshwara, David M. Weld

We investigate the Bose-Hubbard chain in the presence of nearest-neighbor pairing. The pairing term gives rise to an unusual gapped $\mathbb{Z}_2$ Ising phase that has number fluctuation but no off-diagonal long range order. This phase has a strongly correlated many-body doubly degenerate ground state which is effectively a gap-protected macroscopic qubit. I

Liyun Jiang, Lei Nie, Fangrong Yan, Ying Yuan

Tissue-agnostic trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple dise

Sylvie Corteel, Andrew Gitlin, David Keating, Jeremy Meza

We describe a novel Yang-Baxter integrable vertex model. From this vertex model we construct a certain class of partition functions that we show are equal to the LLT polynomials of Lascoux, Leclerc, and Thibon. Using the vertex model formalism, we give alternate proofs of many properties of these polynomials, including symmetry and a Cauchy identity.

Yun Soo Myung, De-Cheng Zou

We perform the stability analysis of the Kerr black hole in the Einstein-Chern-Simons-Scalar theory with quadratic scalar coupling. For positive coupling parameter ($α>0$), we introduce the (2+1)-dimensional hyperboloidal foliation method to show that the Kerr black hole is unstable against scalar-mode perturbation. In case of $α<0$, it is shown that the Ker

Shivangi Yadav, Arun Ross

In this work, we propose a novel Cyclic Image Translation Generative Adversarial Network (CIT-GAN) for multi-domain style transfer. To facilitate this, we introduce a Styling Network that has the capability to learn style characteristics of each domain represented in the training dataset. The Styling Network helps the generator to drive the translation of im

Tatsuhiko Shirai, Shu Tanaka, Nozomu Togawa

We propose a novel type of minor-embedding (ME) in simulated-annealing-based Ising machines. The Ising machines can solve combinatorial optimization problems. Many combinatorial optimization problems are mapped to find the ground (lowest-energy) state of the logical Ising model. When connectivity is restricted on Ising machines, ME is required for mapping fr

Songhai Zhang, Xiangli Li, Yingtian Liu, Hongbo Fu

In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery from monocular videos, we estimate the global scale objects in a video with a Bayesian approach incorporating the size priors of objects, where the scene objects sizes should strictly

Patrick D Fitzgerald

The Kramers-Kronig relations describe a pair of integral transforms relating the real and imaginary components of an analytic function in the complex plane. These relations are particularly useful in extracting refractive index characteristics of a given physical test species, wherein more direct measurements are not terribly reliable. In this report, a meth

Gongbo Liang, Yuanyuan Su, Sheng-Chieh Lin, Yu Zhang

Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in modern astronomy and cosmology. The determination of optical richness can be challenging. We propose a self-supervised appr

Giridhar V. Kulkarni

Since a long-time, the quantum integrable systems have remained an area where modern mathematical methods have given an access to interesting results in the study of physical systems. The exact computations, both numerical and asymptotic, of the correlation function is one of the most important subject of the theory of the quantum integrable models. In this

Nicholas F. Boardman, Gail Zasowski, Jeffrey A. Newman, Sebastian F. Sanchez

Gas-phase abundances and abundance gradients provide much information on past stellar generations, and are powerful probes of how galaxies evolve. Gas abundance gradients in galaxies have been studied as functions of galaxies&#39; mass and size individually, but have largely not been considered across the galaxy mass--size plane. Thus, we investigate gas-pha

Storm Slivkoff, Jack L. Gallant

Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article we demonstrate how this design process can be greatly assisted using an optimization tool known as Mi

Terrance Liu, Paul Pu Liang, Michal Muszynski, Ryo Ishii

Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications towards the early detection and intervention of mental health disorders. One promising data source to help monitor human behavior

Erin Wolf Chambers, Francis Lazarus, Arnaud de Mesmay, Salman Parsa

In this paper we prove that the problem of deciding contractibility of an arbitrary closed curve on the boundary of a 3-manifold is in NP. We emphasize that the manifold and the curve are both inputs to the problem. Moreover, our algorithm also works if the curve is given as a compressed word. Previously, such an algorithm was known for simple (non-compresse

David Hemminger

Let $k$ be a field, let $R$ be a commutative ring, and assume the exponential characteristic of $k$ is invertible in $R$. In this note, we prove that isomorphisms in Voevodsky&#39;s triangulated category of motives $\mathcal{DM}(k;R)$ are detected by motivic homology groups of base changes to all separable finitely generated field extensions of $k$. It then

Jinlu Li

In this paper, we introduce the concepts of m-quasiconvex, originally m-quasiconvex,and generalized m-quasiconvex functionals on topological vector spaces. Then we extend the concept of point separable topological vector spaces (by the topological dual spaces) to quasi-point separable topological vector spaces by families of generalized m-quasiconvex functio

Jianbo Lu, Jiachun Li, Hui Guo, Zhitong Zhuang

A generalized Brans-Dicke (GBD) theory in the framework of Palatini formalism are proposed in this paper. We derive the field equations by using the variational approach and obtain the linearized equations by using the weak-field approximation method. We show various properties of the geometrical scalar field in the Palatini-formalism of GBD theory: it is ma

Ali Ugur Guler, Emir Demirovic, Jeffrey Chan, James Bailey

The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan be solved in two separate stages, it is better to directlyminimize the optimization loss. However, this requires dif-ferentiating through a discrete, non-differentiable combina-to

Pedro Rivero, Ian C. Cloët, Zack Sullivan

The VQE algorithm has turned out to be quite expensive to run given the way we currently access quantum processors (i.e. over the cloud). In order to alleviate this issue, we introduce Quantum Sampling Regression (QSR), an alternative hybrid quantum-classical algorithm, and analyze some of its use cases based on time complexity in the low qubit number regime

Fatemeh Saleh, Sadegh Aliakbarian, Hamid Rezatofighi, Mathieu Salzmann

Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly

Sourav Chakraborty, Nikhil S. Mande, Rajat Mittal, Tulasimohan Molli

Chang&#39;s lemma (Duke Mathematical Journal, 2002) is a classical result with applications across several areas in mathematics and computer science. For a Boolean function $f$ that takes values in {-1,1} let $r(f)$ denote its Fourier rank. For each positive threshold $t$, Chang&#39;s lemma provides a lower bound on $wt(f):=\Pr[f(x)=-1]$ in terms of the dime

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks. In particular, a growing volume of literature has been exploring ways to enforce energy conservation while using neural networks for learning dynamics from observed time-series data. In this work, we survey ten recently propos

Wei Tang, Teague Tomesh, Martin Suchara, Jeffrey Larson

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC&#39;s power, but today&#39;s Noisy Intermediate-Scale Quantum (NISQ) dev

Opeoluwa Owoyele, Pinaki Pal

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach. This has motivated the use of fully connected artificial neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system. Ho

Zhi-Cheng Shi, Hai-Ning Wu, Li-Tuo Shen, Yan Xia

Composite pulses are an efficient tool for robust quantum control. In this work, we derive the form of the composite pulse sequence to implement robust single-qubit gates in a three-level system, where two low-energy levels act as a qubit. The composite pulses can efficiently cancel the systematic errors up to a certain order. We find that the three-pulse se

Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke

This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks. The benchmark draws from the expertise of leading mobile-SoC vendors, ML-framework providers, and model producers. It comprises a suite of models that operate

Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Vinodkumar Prabhakaran

Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations. In this paper, we ask how portable the assumptions of this largely Western take on algorithmic fairness are to a different geo-cultural context such as India. Based on 36 expert interviews with Indian scholars, and an analysis of emerging algorithmic deployments in Ind

D. C. Good, B. C. Andersen, P. Chawla, K. Crowter

We report the discovery of seven new Galactic pulsars with the Canadian Hydrogen Intensity Mapping Experiment&#39;s Fast Radio Burst backend (CHIME/FRB). These sources were first identified via single pulses in CHIME/FRB, then followed up with CHIME/Pulsar. Four sources appear to be rotating radio transients (RRATs), pulsar-like sources with occasional singl

Bach Nguyen, Kurt Trampel, Milen Yakimov

We describe a connection between the subjects of cluster algebras, polynomial identity algebras and discriminants. For this, we define the notion of root of unity quantum cluster algebras and prove that they are polynomial identity algebras. Inside each such algebra we construct a (large) canonical central subalgebra, which can be viewed as a far reaching ge

Lisbeth Carrero, Alexander Quaas

In this paper, we prove existence results of a one-dimensional periodic solution to equations with the fractional Laplacian of order $s\in(1/2,1)$, singular nonlinearity, and gradient term under various situations, including nonlocal contra-part of classical Lienard vector equations, as well other nonlocal versions of classical results know only in the conte

Anirudh Chiti, Anna Frebel, Joshua D. Simon, Denis Erkal

The Milky Way is surrounded by dozens of ultra-faint (< $10^5$ solar luminosities) dwarf satellite galaxies. They are the surviving remnants of the earliest galaxies, as confirmed by their ancient (~13 billion years old) and chemically primitive stars. Simulations suggest that these systems formed within extended dark matter halos and experienced early galax

Diana Mincu, Eric Loreaux, Shaobo Hou, Sebastien Baur

Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse outcomes in electronic health records (EHRs) due to their ability to encode past clinical states. These deep, recurrent architectures have displayed increased performance compared to other modeling approaches in a number of tasks, fueling the interest in deploying deep models

Osvaldo Guzman, Damjan Kalajdzievski

We prove that every MAD family can be destroyed by a proper forcing that preserves $P$-points. With this result, we prove that it is consistent that $ω_{1}=\mathfrak{u}<\mathfrak{a,}$ solving a nearly 20 year old problem of Shelah and a problem of Brendle. We will also present a simple proof of a result of Blass and Shelah that the inequality $\mathfrak{u<s}

Yizhe Huang, Boris Kramer

We propose a new framework to design controllers for high-dimensional nonlinear systems. The control is designed through the iterative linear quadratic regulator (ILQR), an algorithm that computes control by iteratively applying the linear quadratic regulator on the local linearization of the system at each time step. Since ILQR is computationally expensive,

Francisco McGee, Quentin Novinger, Ronald M. Levy, Vincenzo Carnevale

Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict the effect of mutations. Despite encouraging results, quantitative characterization and comparison of GPSM-generated probability distributions is still lacking. It is currently unclear whethe

Scott McCullough, Nicole Tuovila

Free spectrahedra are natural objects in the theories of operator systems and spaces and completely positive maps. They also appear in various engineering applications. In this paper, free spectrahedra satisfying a Reinhardt symmetry condition are characterized graph theoretically. It is also shown that, for a simple class of such spectrahedra, automorphisms

Marcus Basalla, Johannes Schneider, Jan vom Brocke

While the potential of deep learning (DL) for automating simple tasks is already well explored, recent research has started investigating the use of deep learning for creative design, both for complete artifact creation and supporting humans in the creation process. In this paper, we use insights from computational creativity to conceptualize and assess curr

Fabian Klute, Irene Parada

A drawing of a graph is $k$-plane if no edge is crossed more than $k$ times. In this paper we study saturated $k$-plane drawings with few edges. This are $k$-plane drawings in which no edge can be added without violating $k$-planarity. For every number of vertices $n>k+1$, we present a tight construction with $\frac{n-1}{k+1}$ edges for the case in which the

Bahman Angoshtari, Erhan Bayraktar, Virginia R. Young

We formulate and solve a deterministic optimal consumption problem to maximize the discounted CRRA utility of an individual's consumption-to-habit process assuming she only invests in a riskless market and that she is unwilling to consume at a rate below a certain proportion $\alpha\in(0,1]$ of her consumption habit. Increasing $\alpha$, increases the degree

Klas Pettersson, Andrey Karzhou, Irina Pettersson

The Helmholtz equation has been used for modelling the sound pressure field under a harmonic load. Computing harmonic sound pressure fields by means of solving Helmholtz equation can quickly become unfeasible if one wants to study many different geometries for ranges of frequencies. We propose a machine learning approach, namely a feedforward dense neural ne

Yansong Qian, Yanqin Wu

The sizes of small planets have been known to be bi-modal, with a gap separating planets that have lost their primordial atmospheres (super-Earths), and the ones that retain them (mini-Neptunes). Here, we report evidences for another distinct population at smaller sizes. By focussing on planets orbiting around GK-dwarfs inward of 16 days, and correcting for

Junsoo Park, Max Dylla, Yi Xia, Max Wood

Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With

Anna Savelyeva

We prove that the automorphism groups of Hopf manifolds are Jordan.

Sachin Maheshwari, Spyros Stathopoulos, Jiaqi Wang, Alexander Serb

RRAM technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the development of an accessible and user-friendly design flow, supported by an industry-grade toolchain. In this work, we demon

Ying Chen, Xu Ouyang, Kaiyue Zhu, Gady Agam

Semantic segmentation has achieved significant advances in recent years. While deep neural networks perform semantic segmentation well, their success rely on pixel level supervision which is expensive and time-consuming. Further, training using data from one domain may not generalize well to data from a new domain due to a domain gap between data distributio

Dhvanil D. Desai, Barbara S. Ryden

Using data from the Sloan Digital Sky Survey (SDSS) Legacy Survey, we study the alignment of luminous galaxies with spectroscopic data with the surrounding larger-scale structure as defined by galaxies with only photometric data. We find that galaxies from the red sequence have a statistically significant tendency for their apparent long axes to align parall

Michael Freedman, Matthew B. Hastings

We give a procedure for &#34;reverse engineering&#34; a closed, simply connected, Riemannian manifold with bounded local geometry from a sparse chain complex over $\mathbb{Z}$. Applying this procedure to chain complexes obtained by &#34;lifting&#34; recently developed quantum codes, which correspond to chain complexes over $\mathbb{Z}_2$, we construct the fi

Bingzhi Zhang, Quntao Zhuang

Quantum networks will play a key role in distributed quantum information processing. As the network size increases, network-level errors like random breakdown and intentional attack are inevitable; therefore, it is important to understand the robustness of large-scale quantum networks, similar to what has been done for the classical counterpart---the Interne

Jörg Hennig, Rodrigo Panosso Macedo

We study axisymmetric solution to the conformally invariant wave equation on a Kerr background by means of numerical and analytical methods. Our main focus is on the behaviour of the solutions near spacelike infinity, which is appropriately represented as a cylinder. Earlier studies of the wave equation on a Schwarzschild background have revealed important d

Mehrdad Yousefzadeh, Yinuo Yao, Ilenia Battiato

A simulation framework based on the level-set and the immersed boundary methods (LS-IBM) has been developed for reactive transport problems in porous media involving a moving solid-fluid interface. The interface movement due to surface reactions is tracked by the level-set method, while the immersed boundary method captures the momentum and mass transport at

Tomislav Maric

A straightforward and computationally efficient Consecutive Cubic Spline (CCS) iterative algorithm is proposed for positioning the planar interface of the unstructured geometrical Volume-of-Fluid method in arbitrarily-shaped cells. The CCS algorithm is a two-point root-finding algorithm specifically designed for the VOF interface positioning problem, where t

Md. Saif Hassan Onim, Muhaiminul Islam Akash, Mahmudul Haque, Raiyan Ibne Hafiz

Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate of the vehicles of Bangladesh and recogni

Jonathan M. DeStefano, Lin-Lin Wang

Recent studies have predicted that magnetic EuCd2As2 can host several different topological states depending on its magnetic order, including a single pair of Weyl points. Here we report on the bulk properties and band inversion induced by pressure in the non-magnetic analogs AECd2As2 (AE = Ca, Sr, Ba) as studied with density functional theory calculations.

Sujoy Bhore, Csaba D. Tóth

Lightness is a fundamental parameter for Euclidean spanners; it is the ratio of the spanner weight to the weight of the minimum spanning tree of a finite set of points in $\mathbb{R}^d$. In a recent breakthrough, Le and Solomon (2019) established the precise dependencies on $\varepsilon>0$ and $d\in \mathbb{N}$ of the minimum lightness of $(1+\varepsilon)$-s

Bartosz Regula, Ryuji Takagi

We develop a unified framework to characterize one-shot transformations of dynamical quantum resources in terms of resource quantifiers, establishing universal conditions for exact and approximate transformations in general resource theories. Our framework encompasses all dynamical resources represented as quantum channels, including those with a specific st

Zheng Huang, Marcello Lucia, Gabriella Tarantello

In this paper we define a Donaldson type functional whose Euler-Lagrange equations are a system of differential equations which corresponds to Hitchin's self-duality equations for a suitable choice of Higgs bundle on closed Riemann surfaces. The main challenge of this functional is its lack of regularity and lack of compactness when defined in its natural do

Pedro Felzenszwalb, Caroline Klivans, Alice Paul

We introduce a fixed point iteration process built on optimization of a linear function over a compact domain. We prove the process always converges to a fixed point and explore the set of fixed points in various convex sets. In particular, we consider elliptopes and derive an algebraic characterization of their fixed points. We show that the attractive fixe

Yuval Filmus, Or Meir, Avishay Tal

$\newcommand{\ACz}{\mathbf{AC}^0}$ H\r{a}stad showed that any De Morgan formula (composed of AND, OR and NOT gates) shrinks by a factor of $\tilde{O}(p^{2})$ under a random restriction that leaves each variable alive independently with probability $p$ [SICOMP, 1998]. Using this result, he gave an $\widetilde{\Omega}(n^{3})$ formula size lower bound for the A

Lucy M. Thomas, Patricia Schmidt, Geraint Pratten

Accurately modelling the complete gravitational-wave signal from precessing binary black holes through the late inspiral, merger and ringdown remains a challenging problem. The lack of analytic solutions for the precession dynamics of generic double-spin systems, and the high dimensionality of the problem, obfuscate the incorporation of strong-field spin-pre

Zhengzheng Zhai, Leo Radzihovsky

Motivated by striped correlated quantum matter, and the recently developed duality between elasticity of a two-dimensional (2D) crystal and a gauge theory, we derive a dual coupled U(1) vector gauge theory for a two-dimensional (2D) quantum smectic, where the disclination is mapped onto the fractonic charge, that we demonstrate can only move transversely to

Diptajyoti Mukherjee, Qirong Zhu, Hy Trac, Carl L. Rodriguez

Direct $N$-body simulations of star clusters are accurate but expensive, largely due to the numerous $\mathcal{O} (N^2)$ pairwise force calculations. To solve the post-million-body problem, it will be necessary to use approximate force solvers, such as tree codes. In this work, we adapt a tree-based, optimized Fast Multipole Method (FMM) to the collisional $

Daisei Abe, Tsuyoshi Inoue, Shu-ichiro Inutsuka, Tomoaki Matsumoto

Recent observations of molecular clouds show that dense filaments are the sites of present-day star formation. Thus, it is necessary to understand the filament formation process because these filaments provide the initial condition for star formation. Theoretical research suggests that shock waves in molecular clouds trigger filament formation. Since several